Abstract-Five methods that generate multiple prototypes from labeled data are reviewed. Then we introduce a new sixth approach, which is a modification of Chang's method. We compare the six methods with two standard classifier designs: the 1-nearest prototype (1-np) and 1-nearest neighbor (1-nn) rules. The standard of comparison is the resubstitution error rate; the data used are the Iris data. Our modified Chang's method produces the best consistent (zero errors) design. One of the competitive learning models produces the best minimal prototypes design (five prototypes that yield three resubstitution errors).Index Terms-Competitive learning, Iris data, modified Chang's method (MCA), multiple prototypes, nearest neighbor (1-nn) rule.

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Electronic concept mapping tools empower experts to play an active role in the knowledge capture process, and provide a medium for building richly connected multimedia knowledge models-sets of linked concept maps and resources about a particular domain. Knowledge models are intended to be used as a means for sharing knowledge among humans, not as carefully-crafted knowledge bases upon which machines will be performing inference. However, users must still confront the questions of what to include in a concept map and which concept maps to include in a knowledge model. This paper describes ongoing research on methods to provide content-based support to users as they extend concept maps by adding concepts and propositions, and as they select topics for new maps. The goal is to provide scaffolding for experts as they build their own concept maps, link their maps to others', and decide how to extend their knowledge models. The paper presents three approaches which start from a concept map under construction and mine related information-both from prior concept maps, and from the web-to propose information to aid the user's knowledge capture and knowledge construction. The paper begins with a brief summary of the concept mapping process and the CmapTools concept mapping software. It then presents three types of implemented suggesters, to suggest concepts, propositions, concept maps, and new topics to aid experts using the CmapTools, and describes preliminary experiments to assess their performance. It closes with a discussion of next steps for testing and refining these methods.

Artificial intelligence (AI) methods have the potential for broad impact in smart homes. Different AI methods offer different contributions for this domain, with different design goals, tasks, and circumstances dictating where each type of method best applies. In this chapter, we describe motivations and opportunities for applying case-based reasoning (CBR) to a human-centered approach to the capture, sharing, and revision of knowledge for smart homes. Starting from the CBR cognitive model of reasoning and learning, we illustrate how CBR could provide useful capabilities for problem detection and response, provide a basis for personalization and learning, and provide a paradigm for homehuman communication to cooperatively guide performance improvement. The episodic memory developed by case-based reasoning systems within the home can both guide behaviors by the smart home and help its inhabitants to augment their own memories. After sketching how these capabilities could be served by case-based reasoning, the chapter discusses some design issues for applying CBR within smart homes and case-based reasoning research challenges for realizing the vision.